Method and device for evaluating comprehensive performance of battery

Abstract

The invention provides a method and a device for evaluating the comprehensive performance of a battery, and relates to the technical field of batteries. The method comprises the following steps: selecting a plurality of first evaluation indexes and a plurality of second evaluation indexes from battery performance parameters; performing battery performance evaluation on the initial to-be-evaluated battery based on the plurality of first evaluation indexes, and obtaining an initial evaluation result of the initial to-be-evaluated battery; according to the initial evaluation result, obtaining the initial to-be-evaluated battery passing the initial evaluation as a target to-be-evaluated battery; constructing a virtual optimal battery sample and a virtual worst battery sample based on the index requirements of the plurality of second evaluation indexes; and establishing an entropy weight-based TOPSIS model based on the second evaluation index data corresponding to the target to-be-evaluated battery, the virtual optimal battery sample and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target to-be-evaluated battery. Performance normalization comparison of the batteries of different specifications is achieved, the method is suitable for performance evaluation of the batteries of different specifications, and human factor fixed weight intervention can be effectively avoided.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method and device for evaluating the comprehensive performance of a battery. Background Art

[0002] With the rapid development of energy storage technology, lithium-ion batteries of various brands and specifications have emerged on the market. When selecting new batteries, energy storage system integrators usually conduct comprehensive tests in accordance with national standards. Only when all test results of a battery meet national standards will it be included in the candidate list. In recent years, with the continuous advancement of battery manufacturing technology, products from different manufacturers can generally meet the requirements of national standards. However, when comparing batteries of different capacity specifications (such as 306Ah, 314Ah, 315Ah, 320Ah, etc.), there is a lack of a scientific method to support the evaluation process. Summary of the invention

[0003] The present disclosure provides a method and device for evaluating the comprehensive performance of a battery, so as to at least solve the problem in the related art that there is a lack of a scientific method to support the evaluation process when comparing batteries of different capacity specifications.

[0004] The first aspect of the present application proposes a method for evaluating the comprehensive performance of a battery, comprising: selecting multiple first evaluation indicators and multiple second evaluation indicators from battery performance parameters; performing battery performance evaluation on an initial battery to be evaluated based on the multiple first evaluation indicators to obtain an initial evaluation result of the initial battery to be evaluated; according to the initial evaluation result, obtaining an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated; constructing a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of multiple second evaluation indicators; establishing an entropy-weighted TOPSIS model based on the second evaluation indicator data corresponding to each of the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

[0005] According to one embodiment of the present application, an entropy-weighted TOPSIS model is established based on the second evaluation index data corresponding to each of the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample to obtain a comprehensive evaluation result of the target battery to be evaluated, including: forward processing the second evaluation index data corresponding to each of the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample; establishing an index data matrix based on the second evaluation index data after the forward processing; calculating the information entropy of each second evaluation index according to the index data matrix, thereby obtaining the entropy weight of each second evaluation index; standardizing the index data matrix to obtain a standardized matrix, and constructing a weighted matrix in combination with the entropy weight of each second evaluation index; obtaining the optimal ideal solution and the worst ideal solution based on the weighted matrix, and obtaining the distance between the target battery to be evaluated and the optimal ideal solution and the worst ideal solution, respectively, to obtain the comprehensive evaluation result of the target battery to be evaluated.

[0006] According to one embodiment of the present application, the second evaluation indicator data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample are respectively processed positively, including: judging the data type of each second evaluation indicator, the data type is extremely large data, extremely small data, intermediate data or interval data; obtaining the positive processing method corresponding to each data type, and performing positive processing on the second evaluation indicator data corresponding to the data type based on the positive processing method.

[0007] According to one embodiment of the present application, the method for establishing the indicator data matrix based on the second evaluation indicator data after the forward processing is as follows:

[0008] Assume that there are n battery samples and p second evaluation indicators, where the n battery samples include 1 virtual optimal battery sample, 1 virtual worst battery sample and n-2 target batteries to be evaluated. The indicator data matrix X is as follows:

[0009]

[0010] Among them, in the indicator data matrix, x ij Represents the second evaluation index data of the i-th battery sample after the positive processing corresponding to the j-th second evaluation index, where i=1, 2, ...n; j=1, 2, ...p.

[0011] According to one embodiment of the present application, the information entropy of each second evaluation indicator is calculated according to the indicator data matrix, so as to obtain the entropy weight of each second evaluation indicator, including:

[0012] Calculate the proportion of the i-th battery sample in the j-th second evaluation index, and replace the data with a proportion of 0 with a preset value, which is set to 0.0001. The formula is expressed as:

[0013]

[0014]

[0015] Based on the ratio, the information entropy of each second evaluation index is calculated, and the formula is expressed as:

[0016]

[0017] The information redundancy value of each second evaluation index is calculated based on information entropy, and the formula is expressed as follows:

[0018] d j =1-e j

[0019] The entropy weight of each second evaluation index is determined based on the information redundancy value, and the formula is expressed as follows:

[0020]

[0021] Among them, i=1,2,…n; j=1,2,…p.

[0022] According to one embodiment of the present application, the indicator data matrix is ​​standardized to obtain a standardized matrix, and a weighted matrix is ​​constructed in combination with the entropy weight of each second evaluation indicator, including:

[0023] The indicator data matrix is ​​standardized to obtain the standardized matrix Z. Each element in the standardized matrix Z is expressed as:

[0024]

[0025] Based on the standardized matrix Z and combined with the entropy weight of each second evaluation indicator, a weighted matrix Z is constructed * , weight matrix Z * Each element in is represented as:

[0026] z ij * =z ij ·w j

[0027] Among them, i=1,2,…n; j=1,2,…p.

[0028] According to one embodiment of the present application, the optimal ideal solution and the worst ideal solution are obtained based on the weighted matrix, and the distances between the target battery to be evaluated and the optimal ideal solution and the worst ideal solution are obtained to obtain a comprehensive evaluation result of the target battery to be evaluated, including:

[0029] Obtain the optimal ideal solution Z based on the weighted matrix *+ :

[0030] Z *+ =(z1 *+ ,z2 *+ ,…,z p *+ )

[0031] =(max{z 11 * ,z 21 * ,…,z n1 *},max{z 12 * ,z 22 * ,…,z n2 *},…,max{z 1p * ,z 2p * ,…,z np *})

[0032] Obtain the worst ideal solution Z based on the weighted matrix *- :

[0033] Z *- =(z1 *- ,z2 *- ,…,z p *- )

[0034] =(min{z 11 * ,z 21 * ,…,z n1 *},min{z 12 * ,z 22 * ,…,z n2 *},…,min{z 1p * ,z 2p * ,…,z np *})

[0035] Get the distance between the i-th battery sample and the optimal ideal solution:

[0036]

[0037] Get the distance between the i-th battery sample and the worst ideal solution:

[0038]

[0039] Calculate the relative closeness between the i-th battery sample and the optimal ideal solution:

[0040]

[0041] Obtaining a sample number of a target battery to be evaluated, and determining a relative closeness between the target battery to be evaluated and an optimal ideal solution according to the sample number;

[0042] A comprehensive evaluation result of the target battery to be evaluated is generated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution.

[0043] According to one embodiment of the present application, a comprehensive evaluation result of the target battery to be evaluated is generated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution, including: comparing the relative proximity between the target battery to be evaluated and the optimal ideal solution with a preset relative proximity level interval to obtain a comparison result; determining the performance level of the target battery to be evaluated based on the comparison result, and using the performance level of the target battery to be evaluated as the comprehensive evaluation result of the target battery to be evaluated.

[0044] The second aspect of the present application proposes a device for evaluating the comprehensive performance of a battery, comprising: an indicator selection module, used to select multiple first evaluation indicators and multiple second evaluation indicators from battery performance parameters; a first evaluation module, used to perform battery performance evaluation on an initial battery to be evaluated based on multiple first evaluation indicators, and obtain an initial evaluation result of the initial battery to be evaluated; an acquisition module, used to obtain, according to the initial evaluation result, an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated; a construction module, used to construct a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of multiple second evaluation indicators; a second evaluation module, used to establish an entropy-weighted TOPSIS model based on the second evaluation indicator data corresponding to each of the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

[0045] According to one embodiment of the present application, the second evaluation module is also used to: perform forward processing on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample; establish an index data matrix based on the second evaluation index data after the forward processing; calculate the information entropy of each second evaluation index according to the index data matrix, so as to obtain the entropy weight of each second evaluation index; standardize the index data matrix to obtain a standardized matrix, and construct a weighted matrix based on the entropy weight of each second evaluation index; obtain the optimal ideal solution and the worst ideal solution based on the weighted matrix, and obtain the distance between the target battery to be evaluated and the optimal ideal solution and the worst ideal solution respectively to obtain the comprehensive evaluation result of the target battery to be evaluated.

[0046] According to one embodiment of the present application, the second evaluation module is also used to: determine the data type of each second evaluation indicator, the data type is extremely large data, extremely small data, intermediate data or interval data; obtain the forward processing method corresponding to each data type, and perform forward processing on the second evaluation indicator data corresponding to the data type based on the forward processing method.

[0047] According to one embodiment of the present application, in the second evaluation module, the method for establishing the indicator data matrix based on the second evaluation indicator data after the forward processing is as follows:

[0048] Assume that there are n battery samples and p second evaluation indicators, where the n battery samples include 1 virtual optimal battery sample, 1 virtual worst battery sample and n-2 target batteries to be evaluated. The indicator data matrix X is as follows:

[0049]

[0050] Among them, in the indicator data matrix, x ij Represents the second evaluation index data of the i-th battery sample after the positive processing corresponding to the j-th second evaluation index, where i=1, 2, ...n; j=1, 2, ...p.

[0051] According to one embodiment of the present application, the second evaluation module is further used to calculate the proportion of the i-th battery sample in the j-th second evaluation index, and replace the data with a proportion of 0 with a preset value, the preset value is set to 0.0001, and the formula is expressed as:

[0052]

[0053]

[0054] Based on the ratio, the information entropy of each second evaluation index is calculated, and the formula is expressed as:

[0055]

[0056] The information redundancy value of each second evaluation index is calculated based on information entropy, and the formula is expressed as follows:

[0057] d j =1-e j

[0058] The entropy weight of each second evaluation index is determined based on the information redundancy value, and the formula is expressed as follows:

[0059]

[0060] Among them, i=1,2,…n; j=1,2,…p.

[0061] According to one embodiment of the present application, the second evaluation module is further used to: standardize the indicator data matrix to obtain a standardized matrix Z, where each element in the standardized matrix Z is expressed as:

[0062]

[0063] Based on the standardized matrix Z and combined with the entropy weight of each second evaluation indicator, a weighted matrix Z is constructed * , weight matrix Z * Each element in is represented as:

[0064] z ij * =z ij ·w j

[0065] Among them, i=1,2,…n; j=1,2,…p.

[0066] According to one embodiment of the present application, the second evaluation module is further used to: obtain the optimal ideal solution Z based on the weighted matrix *+ :

[0067] Z *+ =(z1 *+ ,z2 *+ ,…,z p *+ )

[0068] =(max{z 11 * ,z 21 * ,…,z n1 *},max{z 12 * ,z 22 * ,…,z n2*},…,max{z 1p * ,z 2p * ,…,z np *})

[0069] Obtain the worst ideal solution Z based on the weighted matrix *- :

[0070] Z *- =(z1 *- ,z2 *- ,…,z p *- )

[0071] =(min{z 11 * ,z 21 * ,…,z n1 *},min{z 12 * ,z 22 * ,…,z n2 *},…,min{z 1p * ,z 2p * ,…,z np *})

[0072] Get the distance between the i-th battery sample and the optimal ideal solution:

[0073]

[0074] Get the distance between the i-th battery sample and the worst ideal solution:

[0075]

[0076] Calculate the relative closeness between the i-th battery sample and the optimal ideal solution:

[0077]

[0078] Obtaining a sample number of a target battery to be evaluated, and determining a relative closeness between the target battery to be evaluated and an optimal ideal solution according to the sample number;

[0079] A comprehensive evaluation result of the target battery to be evaluated is generated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution.

[0080] According to one embodiment of the present application, the second evaluation module is further used to: compare the relative proximity between the target battery to be evaluated and the optimal ideal solution with a preset relative proximity level interval to obtain a comparison result; determine the performance level of the target battery to be evaluated based on the comparison result, and use the performance level of the target battery to be evaluated as the comprehensive evaluation result of the target battery to be evaluated.

[0081] The third aspect embodiment of the present application proposes an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to implement the method for evaluating the comprehensive performance of a battery as described in the first aspect embodiment of the present application.

[0082] The fourth aspect embodiment of the present application proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the comprehensive performance evaluation method of the battery as described in the first aspect embodiment of the present application.

[0083] The fifth aspect embodiment of the present application proposes a computer program product, including a computer program, which, when executed by a processor, implements the method for evaluating the comprehensive performance of a battery as described in the first aspect embodiment of the present application.

[0084] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects: the present application evaluates the battery objectively and scientifically, realizes the normalized comparison of the performance of batteries of different specifications, is applicable to the performance evaluation of batteries of different specifications, and can effectively avoid the intervention of human factors in weight determination; at the same time, a two-level evaluation mechanism is used, firstly, the battery is evaluated by the first evaluation index to select the batteries that meet the standard requirements, and the batteries that meet the standard requirements are comprehensively evaluated; by screening the requirements of the items that meet the requirements, the batteries that do not meet the requirements are effectively avoided from participating in the evaluation. In addition, in addition to being applicable to the evaluation of multiple battery samples, the scheme is also applicable to the evaluation of single battery samples. The parameters of a single battery and the parameters of the best and worst solutions jointly establish a TOPSIS model based on entropy weight, which effectively meets the minimum calculation data requirements of the entropy weight TOPSIS method and avoids the disadvantage that the entropy weight TOPSIS method cannot evaluate a single sample.

[0085] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0087] Figure 1It is an exemplary schematic diagram of a method for evaluating the comprehensive performance of a battery shown in one embodiment of the present application.

[0088] Figure 2 It is an exemplary schematic diagram of a method for evaluating the comprehensive performance of a battery shown in one embodiment of the present application.

[0089] Figure 3 It is a schematic diagram of a device for evaluating the comprehensive performance of a battery shown in one embodiment of the present application.

[0090] Figure 4 It is a schematic diagram of an electronic device shown in one embodiment of the present application. DETAILED DESCRIPTION

[0091] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0092] It should be noted that the acquisition, transmission, storage, use, and processing of data in the technical solution of this application are in compliance with the relevant provisions of national laws and regulations.

[0093] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0094] Figure 1 is an exemplary schematic diagram of a method for evaluating the comprehensive performance of a battery shown in the present application, such as Figure 1 As shown, the method for evaluating the comprehensive performance of the battery comprises the following steps:

[0095] S101, selecting a plurality of first evaluation indicators and a plurality of second evaluation indicators from battery performance parameters.

[0096] The first evaluation index and the second evaluation index can be selected from basic evaluation items of the battery industry, or customized test items can be selected.

[0097] Among them, the first evaluation index mainly selects basic performance indicators to screen out batteries that meet the basic standard requirements of batteries to enter the second level evaluation.

[0098] Optionally, customized test items may include safety testing, performance testing, environmental adaptability testing, etc.

[0099] Among them, safety tests: such as overcharge test, short circuit test, vibration test, drop test, etc., ensure the safety of the battery under various conditions.

[0100] Among them, performance tests: such as capacity test, internal resistance test, charge and discharge efficiency test, cycle life test, etc., evaluate the actual performance and durability of the battery.

[0101] Among them, environmental adaptability tests: such as high temperature, low temperature test, humidity test, etc., ensure the stability of the battery under different environmental conditions.

[0102] Optionally, the first evaluation index and the second evaluation index can be selected from evaluation indexes such as safety (such as overcharge protection, short circuit protection), electrochemical performance (such as discharge capacity, internal resistance), cycle life, energy density, cost-effectiveness, temperature characteristics, and self-discharge rate.

[0103] Among them, the first evaluation index and the second evaluation index generally do not have overlapping evaluation indexes, but some overlapping indexes may appear according to the specific settings of the user, and this application does not limit this.

[0104] S102, performing battery performance evaluation on an initial battery to be evaluated based on a plurality of first evaluation indicators to obtain an initial evaluation result of the initial battery to be evaluated.

[0105] After selecting a plurality of first evaluation indicators, the battery performance of the initial battery to be evaluated is evaluated in turn based on the plurality of first evaluation indicators, an evaluation result of each first evaluation indicator corresponding to the initial battery to be evaluated is obtained, and an initial evaluation result corresponding to the initial battery to be evaluated is generated according to the evaluation result of each first evaluation indicator corresponding to the initial battery to be evaluated.

[0106] Wherein, for any initial battery to be evaluated, if the evaluation results of each first evaluation indicator corresponding to the initial battery to be evaluated are all evaluated as passed, then the initial evaluation result of the initial battery to be evaluated is determined to be evaluated as passed.

[0107] Among them, for any initial battery to be evaluated, if there is at least one first evaluation indicator whose evaluation result is evaluation failure, the initial evaluation result of the initial battery to be evaluated is determined to be evaluation failure, and the evaluation failure is used as the final evaluation result of the initial battery to be evaluated. It can also be understood that the initial battery to be evaluated has failed the evaluation in the first level evaluation and is not eligible to enter the second level evaluation.

[0108] The initial battery to be evaluated may be a single battery or a batch of multiple batteries, which is not limited in this application.

[0109] For example, if 20 evaluation indicators are selected as the first evaluation indicators, after the initial battery A to be evaluated is evaluated for these 20 first evaluation indicators, the result obtained is that all the 20 first evaluation indicators are evaluated as passed, then the initial evaluation result of the initial battery A to be evaluated is determined to be passed.

[0110] Exemplarily, if 20 evaluation indicators are selected as the first evaluation indicators, after the initial battery B to be evaluated is evaluated for these 20 first evaluation indicators, the result obtained is that 19 first evaluation indicators pass the evaluation and 1 first evaluation indicator fails the evaluation, then the initial evaluation result of the battery B to be initially evaluated is determined to be an evaluation failure, and the evaluation failure is used as the final evaluation result of the initial battery B to be evaluated.

[0111] S103, according to the initial evaluation result, obtaining an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated.

[0112] If the initial battery to be evaluated is a battery, if the battery passes the initial evaluation, the battery will be used as the target battery to be evaluated to enter the second round of evaluation; if the battery fails the initial evaluation, the evaluation ends, and the final evaluation result of the battery is determined to be that the initial evaluation fails, and there is no need to enter the second round of evaluation.

[0113] If the initial batteries to be evaluated are a batch of multiple batteries, then according to the initial evaluation results, the initial batteries to be evaluated that pass the initial evaluation are obtained as the target batteries to be evaluated to enter the second round of evaluation. For example, if there are 10 batteries to be evaluated initially, of which 4 batteries fail the initial evaluation, then the final evaluation results of these 4 batteries are determined to be failure to pass the initial evaluation; if 6 batteries pass the initial evaluation, then these 6 batteries are determined as target batteries to be evaluated to enter the second round of evaluation.

[0114] S104, constructing a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of multiple second evaluation indicators.

[0115] A virtual worst battery sample is constructed according to the minimum requirement standard among the indicator requirements of multiple second evaluation indicators.

[0116] According to the highest requirement standard among the indicator requirements of multiple second evaluation indicators, a virtual optimal battery sample under the most ideal conditions is constructed. For example, the energy efficiency class parameter of the virtual optimal battery sample is set to 100%, the ratio of the range to the average value is set to 0, and for other second evaluation indicators with infinite upper limits, a fixed value that can be achieved under ideal conditions is taken upward based on the second evaluation indicator.

[0117] It is not difficult to understand that the virtual optimal battery sample and the virtual worst battery sample are not real battery samples, but a virtual sample concept proposed for the subsequent establishment of the TOPSIS model based on entropy weight.

[0118] S105, establishing an entropy-weighted TOPSIS model based on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

[0119] The second evaluation index data corresponding to the target battery to be evaluated under each second evaluation index is obtained.

[0120] The second evaluation index data under each second evaluation index corresponding to the virtual optimal battery sample is obtained.

[0121] The second evaluation index data corresponding to the virtual worst battery sample under each second evaluation index is obtained.

[0122] According to the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample, a TOPSIS model based on entropy weight is established to obtain the comprehensive evaluation result of the target battery to be evaluated.

[0123] The embodiment of the present application proposes a method for evaluating the comprehensive performance of a battery, including: selecting multiple first evaluation indicators and multiple second evaluation indicators from battery performance parameters; performing battery performance evaluation on the initial battery to be evaluated based on multiple first evaluation indicators to obtain the initial evaluation result of the initial battery to be evaluated; according to the initial evaluation result, obtaining the initial battery to be evaluated that passes the initial evaluation as the target battery to be evaluated; constructing a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of multiple second evaluation indicators; establishing a TOPSIS model based on entropy weight based on the second evaluation indicator data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample, so as to obtain the comprehensive evaluation result of the target battery to be evaluated. The present application objectively and scientifically evaluates the battery, realizes the normalized comparison of the performance of batteries of different specifications, is suitable for the performance evaluation of batteries of different specifications, and can effectively avoid the intervention of human factors in weight determination; at the same time, a two-level evaluation mechanism is used, firstly, the first evaluation indicator is evaluated on the battery to select the battery that meets the standard requirements, and the battery that meets the standard requirements is comprehensively evaluated; through the first evaluation indicator screening, the evaluation of batteries that do not meet the requirements is effectively avoided. In addition, in addition to being applicable to the evaluation of multiple battery samples, this scheme is also applicable to the evaluation of single battery samples. The parameters of a single battery and the parameters of the optimal and worst solutions are used together to establish an entropy-weighted TOPSIS model, which effectively meets the minimum calculation data requirements of the entropy-weighted TOPSIS method and avoids the shortcoming of the entropy-weighted TOPSIS method that it cannot evaluate a single sample.

[0124] Figure 2 is an exemplary schematic diagram of a method for evaluating the comprehensive performance of a battery shown in the present application, such as Figure 2 As shown, the method for evaluating the comprehensive performance of the battery comprises the following steps:

[0125] S201, selecting a plurality of first evaluation indicators and a plurality of second evaluation indicators from battery performance parameters.

[0126] S202, performing battery performance evaluation on an initial battery to be evaluated based on a plurality of first evaluation indicators to obtain an initial evaluation result of the initial battery to be evaluated.

[0127] S203, according to the initial evaluation result, obtaining an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated.

[0128] S204: construct a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of multiple second evaluation indicators.

[0129] Regarding the specific implementation scheme of steps S201 to S204, reference may be made to the specific introduction of the relevant parts in the above embodiment, which will not be elaborated here.

[0130] S205, performing positive processing on the second evaluation index data corresponding to the target battery to be evaluated, the virtual best battery sample and the virtual worst battery sample respectively.

[0131] Determine the data type of each second evaluation indicator, which data type is extremely large data (for example, a second evaluation indicator corresponding to requirements such as 'not less than', 'should not be less than'), extremely small data (for example, a second evaluation indicator corresponding to requirements such as 'not greater than', 'should not be greater than'), intermediate data or interval data.

[0132] A forward processing method corresponding to each data type is obtained, and the second evaluation index data corresponding to the data type is forward processed based on the forward processing method to achieve de-unitization of the second evaluation index, and then compared with each other.

[0133] Among them, since extremely large data itself is positive data, there is no need to perform positive processing on the extremely large data, while extremely small data, intermediate data and interval data are negative data, so they need to be processed positively.

[0134] S206, establishing an indicator data matrix based on the second evaluation indicator data after the forward processing.

[0135] Assume that there are n battery samples and p second evaluation indicators, where the n battery samples include 1 virtual optimal battery sample, 1 virtual worst battery sample and n-2 target batteries to be evaluated. The indicator data matrix X is as follows:

[0136]

[0137] Among them, in the indicator data matrix, x ij Represents the second evaluation index data of the i-th battery sample after the positive processing corresponding to the j-th second evaluation index, where i=1, 2, ...n; j=1, 2, ...p.

[0138] Among them, if there is only one target battery to be evaluated, the indicator data matrix X has only three rows of data, corresponding to one virtual optimal battery sample, one virtual worst battery sample and one target battery to be evaluated, thereby realizing the entropy weight TOPSIS method evaluation of a single battery.

[0139] S207, calculating the information entropy of each second evaluation indicator according to the indicator data matrix, so as to obtain the entropy weight of each second evaluation indicator.

[0140] Calculate the proportion of the i-th battery sample in the j-th second evaluation index, and replace the data with a proportion of 0 with a preset value, which is set to 0.0001. The formula is expressed as:

[0141]

[0142]

[0143] Based on the ratio, the information entropy of each second evaluation index is calculated, and the formula is expressed as:

[0144]

[0145] The information redundancy value of each second evaluation index is calculated based on information entropy, and the formula is expressed as follows:

[0146] d j =1-e j

[0147] The entropy weight of each second evaluation index is determined based on the information redundancy value, and the formula is expressed as follows:

[0148]

[0149] Among them, i=1,2,…n; j=1,2,…p.

[0150] S208, standardize the indicator data matrix to obtain a standardized matrix, and construct a weighted matrix in combination with the entropy weight of each second evaluation indicator.

[0151] The indicator data matrix is ​​standardized to obtain the standardized matrix Z. Each element in the standardized matrix Z is expressed as:

[0152]

[0153] Based on the standardized matrix Z and combined with the entropy weight of each second evaluation indicator, a weighted matrix Z is constructed * , weight matrix Z * Each element in is represented as:

[0154] z ij * =z ij ·w j

[0155] Among them, i=1,2,…n; j=1,2,…p.

[0156] S209, obtaining the best ideal solution and the worst ideal solution based on the weighted matrix, and obtaining the distances between the target battery to be evaluated and the best ideal solution and the worst ideal solution respectively to obtain a comprehensive evaluation result of the target battery to be evaluated.

[0157] As a feasible method, since the virtual optimal battery sample and the virtual worst battery sample have been constructed above, the weighted matrix Z can be directly used here. * The optimal ideal solution and the worst ideal solution are determined by the data corresponding to the virtual optimal battery sample and the virtual worst battery sample respectively.

[0158] As another feasible method, the optimal ideal solution Z is obtained based on the weighted matrix *+ , which is expressed as follows:

[0159] Z *+ =(z1 *+ ,z2 *+ ,…,z p *+ 0

[0160] =(max{z 11 * ,z 21 * ,…,z n1 *},max{z 12 * ,z 22 * ,…,z n2 *},…,max{z 1p * ,z 2p* ,…,z np *})

[0161] Obtain the worst ideal solution Z based on the weighted matrix *- , which is expressed as follows:

[0162] Z *- =(z1 *- ,z2 *- ,…,z p *- )

[0163] =(min{z 11 * ,z 21 * ,…,z n1 *},min{z 12 * ,z 22 * ,…,z n2 *},…,min{z 1p * ,z 2p * ,…,z np *})

[0164] Both of the above methods can obtain the optimal ideal solution and the worst ideal solution. After obtaining the optimal ideal solution and the worst ideal solution, the distance between the i-th battery sample and the optimal ideal solution is obtained:

[0165]

[0166] Get the distance between the i-th battery sample and the worst ideal solution:

[0167]

[0168] Calculate the relative closeness between the i-th battery sample and the optimal ideal solution:

[0169]

[0170] Among them, it is not difficult to understand that the C of the virtual optimal battery sample i =1, C of the virtual worst battery sample i =0, the C of the target battery to be evaluated i Between 0 and 1.

[0171] Then, the sample number of the target battery to be evaluated is obtained, and the relative proximity between the target battery to be evaluated and the optimal ideal solution is determined according to the sample number; and a comprehensive evaluation result of the target battery to be evaluated is generated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution.

[0172] Assuming there are 8 battery samples in total, among which the 1st battery sample is the virtual optimal battery sample, the 8th battery sample is the virtual worst battery sample, and the 2nd to 7th battery samples are the target batteries to be evaluated entering the second-level evaluation, then the relative proximity between the 2nd to 7th battery samples and the optimal ideal solution is obtained respectively, and based on the relative proximity, the comprehensive evaluation results of the 2nd to 7th battery samples (target batteries to be evaluated) are generated respectively.

[0173] Optionally, the relative closeness between the target battery to be evaluated and the optimal ideal solution is compared with a preset relative closeness level range to obtain a comparison result; based on the comparison result, the performance level of the target battery to be evaluated is determined, such as a primary battery, a secondary battery, a tertiary battery, etc., and the performance level of the target battery to be evaluated is used as a comprehensive evaluation result of the target battery to be evaluated.

[0174] The embodiment of the present application evaluates the battery objectively and scientifically, realizes the normalized performance comparison of batteries of different specifications, is applicable to the performance evaluation of batteries of different specifications, and can effectively avoid the intervention of human factors in weight determination; at the same time, it has a two-level evaluation mechanism, firstly, the first evaluation index is evaluated on the battery to screen out the batteries that meet the standard requirements, and the batteries that meet the standard requirements are comprehensively evaluated; by screening the requirements of the items that meet the requirements, it effectively avoids the participation of batteries that do not meet the requirements in the evaluation. In addition, in addition to being applicable to the evaluation of multiple battery samples, this scheme is also applicable to the evaluation of single battery samples. The parameters of a single battery and the parameters of the best and worst solutions jointly establish a TOPSIS model based on entropy weights, which effectively meets the minimum calculation data requirements of the entropy weight TOPSIS method and avoids the disadvantage that the entropy weight TOPSIS method cannot evaluate a single sample.

[0175] For example, after calculating the relative proximity between all target batteries to be evaluated and the optimal ideal solution, the C i Sort by size, C i The larger the value, the closer the target battery to be evaluated is to the optimal value. Table 1 shows some examples of comprehensive evaluation results of batteries of different brands and specifications.

[0176] Table 1 Comprehensive evaluation table of 6 types of batteries

[0177] Serial number Evaluation object <![CDATA[D i + ]]> <![CDATA[D i - ]]> <![CDATA[C i ]]> Ranking 1 Virtual optimal battery sample 0 0.293453 1 1 2 Battery 1-314Ah 0.055576 0.238471 0.810995 7 3 Battery 2-314Ah 0.034998 0.259163 0.881026 5 4 Battery 3-314Ah 0.041985 0.251482 0.856934 6 5 Battery 4-320Ah 0.013476 0.279981 0.954078 2 6 Battery 5-306Ah 0.026324 0.267373 0.910372 3 7 Battery 6-306Ah 0.028961 0.26451 0.901315 4 8 Virtual worst battery sample 0.293453 0 0 8

[0178] The results show that the virtual optimal battery sample is the optimal solution (C i=1), the virtual worst battery sample is the worst solution (C i =0).

[0179] According to the relative closeness of the comprehensive evaluation of the target battery to be evaluated obtained in Table 1, it can be seen that the overall comprehensive evaluation results are:

[0180] Virtual optimal battery sample>Battery 4-320Ah>Battery 5-306Ah>Battery 6-306Ah>Battery 2-314Ah>Battery 3-314Ah>Battery 1-314Ah>Virtual worst battery sample, among the target batteries to be evaluated, Battery 4-320Ah has the highest relative proximity, indicating that it has the highest comprehensive evaluation, and Battery 1-314Ah has the lowest comprehensive evaluation.

[0181] Exemplarily, the comprehensive performance evaluation method of the battery is also applicable to evaluating a single sample, calculating its closeness to the virtual optimal battery sample, and thus obtaining a comprehensive evaluation of the battery. Table 2 is a comprehensive evaluation table of a battery:

[0182] Table 2 Comprehensive evaluation table of a battery

[0183] Serial number Evaluation object <![CDATA[D i + ]]> <![CDATA[D i - ]]> <![CDATA[C i ]]> Ranking 1 Virtual optimal battery sample 0 0.514233 1 1 2 Battery 5-306Ah 0.046391 0.46827 0.909862 2 3 Virtual worst battery sample 0.514233 0 0 3

[0184] As shown in the results of Table 2, after evaluating the battery 5-306Ah according to the above method, the proximity of the battery to the optimal solution can be obtained, and a comprehensive evaluation can be made based on this, for example, to obtain the performance level of the battery.

[0185] Figure 3 is a schematic diagram of a battery comprehensive performance evaluation device shown in the present application, such as Figure 3 As shown, the comprehensive performance evaluation device 300 of the battery includes an indicator selection module 301, a first evaluation module 302, an acquisition module 303, a construction module 304 and a second evaluation module 305, wherein:

[0186] The indicator selection module 301 is used to select a plurality of first evaluation indicators and a plurality of second evaluation indicators from the battery performance parameters.

[0187] The first evaluation module 302 is used to perform battery performance evaluation on the initial battery to be evaluated based on a plurality of first evaluation indicators to obtain an initial evaluation result of the initial battery to be evaluated.

[0188] The acquisition module 303 is used to acquire, according to the initial evaluation result, an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated.

[0189] The construction module 304 is used to construct a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of multiple second evaluation indicators.

[0190] The second evaluation module 305 is used to establish an entropy-weighted TOPSIS model based on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

[0191] This device evaluates batteries objectively and scientifically, and achieves normalized comparison of the performance of batteries of different specifications. It is suitable for the performance evaluation of batteries of different specifications, and can effectively avoid the intervention of human factors in weight determination. At the same time, it is divided into a two-level evaluation mechanism. First, the first evaluation index is evaluated on the battery to select the batteries that meet the standard requirements, and the batteries that meet the standard requirements are comprehensively evaluated. By screening the requirements of the items that meet the requirements, it effectively avoids the participation of batteries that do not meet the requirements in the evaluation. In addition, in addition to being applicable to the evaluation of multiple battery samples, this scheme is also applicable to the evaluation of single battery samples. The parameters of a single battery and the parameters of the best and worst solutions jointly establish a TOPSIS model based on entropy weights, which effectively meets the minimum calculation data requirements of the entropy weight TOPSIS method and avoids the disadvantage that the entropy weight TOPSIS method cannot evaluate a single sample.

[0192] Furthermore, the second evaluation module 305 is also used to: perform forward processing on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample; establish an index data matrix based on the second evaluation index data after the forward processing; calculate the information entropy of each second evaluation index according to the index data matrix, so as to obtain the entropy weight of each second evaluation index; standardize the index data matrix to obtain a standardized matrix, and construct a weighted matrix based on the entropy weight of each second evaluation index; obtain the optimal ideal solution and the worst ideal solution based on the weighted matrix, and obtain the distance between the target battery to be evaluated and the optimal ideal solution and the worst ideal solution respectively to obtain the comprehensive evaluation result of the target battery to be evaluated.

[0193] Furthermore, the second evaluation module 305 is also used to: determine the data type of each second evaluation indicator, which data type is extremely large data, extremely small data, intermediate data or interval data; obtain the forward processing method corresponding to each data type, and perform forward processing on the second evaluation indicator data corresponding to the data type based on the forward processing method.

[0194] Furthermore, in the second evaluation module 305, the index data matrix is ​​established based on the second evaluation index data after the forward processing as follows:

[0195] Assume that there are n battery samples and p second evaluation indicators, where the n battery samples include 1 virtual optimal battery sample, 1 virtual worst battery sample and n-2 target batteries to be evaluated. The indicator data matrix X is as follows:

[0196]

[0197] Among them, in the indicator data matrix, x ij Represents the second evaluation index data of the i-th battery sample after the positive processing corresponding to the j-th second evaluation index, where i=1, 2, ...n; j=1, 2, ...p.

[0198] Furthermore, the second evaluation module 305 is further used to calculate the proportion of the i-th battery sample in the j-th second evaluation index, and replace the data with a proportion of 0 with a preset value, the preset value is set to 0.0001, and the formula is expressed as:

[0199]

[0200]

[0201] Based on the ratio, the information entropy of each second evaluation index is calculated, and the formula is expressed as:

[0202]

[0203] The information redundancy value of each second evaluation index is calculated based on information entropy, and the formula is expressed as follows:

[0204] d j =1-e j

[0205] The entropy weight of each second evaluation index is determined based on the information redundancy value, and the formula is expressed as follows:

[0206]

[0207] Among them, i=1,2,…n; j=1,2,…p.

[0208] Furthermore, the second evaluation module 305 is further used to: standardize the indicator data matrix to obtain a standardized matrix Z, where each element in the standardized matrix Z is expressed as:

[0209]

[0210] Based on the standardized matrix Z and combined with the entropy weight of each second evaluation indicator, a weighted matrix Z is constructed * , weight matrix Z * Each element in is represented as:

[0211] z ij * =z ij ·w j

[0212] Among them, i=1,2,…n; j=1,2,…p.

[0213] Furthermore, the second evaluation module 305 is further used to obtain the optimal ideal solution Z based on the weighted matrix *+ :

[0214] Z *+ =(z1 *+ ,z2 *+ ,…,z p *+ )

[0215] =(max{z 11 * ,z 21 * ,…,z n1 *},max{z 12 * ,z 22 * ,…,z n2 *},…,max{z 1p * ,z 2p * ,…,z np *})

[0216] Obtain the worst ideal solution Z based on the weighted matrix *- :

[0217] Z *- =(z1 *- ,z2 *- ,…,z p *- )

[0218] =(min{z 11 * ,z 21 * ,…,z n1 *},min{z 12 * ,z 22 * ,…,z n2 *},…,min{z 1p * ,z 2p * ,…,z np *})

[0219] Get the distance between the i-th battery sample and the optimal ideal solution:

[0220]

[0221] Get the distance between the i-th battery sample and the worst ideal solution:

[0222]

[0223] Calculate the relative closeness between the i-th battery sample and the optimal ideal solution:

[0224]

[0225] Obtaining a sample number of a target battery to be evaluated, and determining a relative closeness between the target battery to be evaluated and an optimal ideal solution according to the sample number;

[0226] A comprehensive evaluation result of the target battery to be evaluated is generated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution.

[0227] Furthermore, the second evaluation module 305 is also used to: compare the relative closeness between the target battery to be evaluated and the optimal ideal solution with a preset relative closeness level interval to obtain a comparison result; determine the performance level of the target battery to be evaluated based on the comparison result, and use the performance level of the target battery to be evaluated as a comprehensive evaluation result of the target battery to be evaluated.

[0228] In order to implement the above embodiment, the present application embodiment also proposes an electronic device 400, such as Figure 4 As shown, the electronic device 400 includes: a processor 401 and a memory 402 communicatively connected to the processor, the memory 402 stores instructions that can be executed by at least one processor, and the instructions are executed by at least one processor 401 to implement the comprehensive performance evaluation method of the battery as shown in the above embodiment.

[0229] In order to implement the above embodiment, the embodiment of the present application also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable the computer to implement the comprehensive performance evaluation method of the battery as shown in the above embodiment.

[0230] In order to implement the above embodiments, the embodiments of the present application also provide a computer program product, including a computer program, which, when executed by a processor, implements the method for evaluating the comprehensive performance of a battery as shown in the above embodiments.

[0231] In the description of the present application, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0232] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0233] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0234] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the solution.

[0235] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for evaluating the comprehensive performance of a battery, characterized in that: include: Selecting a plurality of first evaluation indicators and a plurality of second evaluation indicators from the battery performance parameters; Performing battery performance evaluation on an initial battery to be evaluated based on the multiple first evaluation indicators, and obtaining an initial evaluation result of the initial battery to be evaluated; According to the initial evaluation result, obtaining an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated; Based on the indicator requirements of the plurality of second evaluation indicators, construct a virtual optimal battery sample and a virtual worst battery sample; A TOPSIS model based on entropy weight is established based on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

2. The method according to claim 1, characterized in that: The TOPSIS model based on entropy weight is established based on the second evaluation index data corresponding to each of the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample to obtain a comprehensive evaluation result of the target battery to be evaluated, including: Performing positive processing on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample; Establishing an indicator data matrix based on the second evaluation indicator data after the positive processing; Calculating the information entropy of each second evaluation indicator according to the indicator data matrix, thereby obtaining the entropy weight of each second evaluation indicator; Standardizing the indicator data matrix to obtain a standardized matrix, and constructing a weighted matrix in combination with the entropy weight of each of the second evaluation indicators; The best ideal solution and the worst ideal solution are obtained based on the weighted matrix, and the distances between the target battery to be evaluated and the best ideal solution and the worst ideal solution are obtained respectively, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

3. The method according to claim 2, characterized in that The performing positive processing on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample, and the virtual worst battery sample, respectively, includes: Determine the data type of each of the second evaluation indicators, the data type being extremely large data, extremely small data, intermediate data, or interval data; A positive processing method corresponding to each of the data types is obtained, and positive processing is performed on the second evaluation indicator data corresponding to the data type based on the positive processing method.

4. The method according to claim 2 or 3, characterized in that: The method for establishing the indicator data matrix based on the second evaluation indicator data after the forward processing is as follows: Assume that there are n battery samples and p second evaluation indicators, where the n battery samples include 1 virtual optimal battery sample, 1 virtual worst battery sample and n-2 target batteries to be evaluated. The indicator data matrix X is as follows: Among them, in the indicator data matrix, x ij Represents the second evaluation index data of the i-th battery sample after the positive processing corresponding to the j-th second evaluation index, where i=1, 2, ...n; j=1, 2, ...p.

5. The method according to claim 4, characterized in that The calculating the information entropy of each second evaluation indicator according to the indicator data matrix, thereby obtaining the entropy weight of each second evaluation indicator, includes: Calculate the proportion of the i-th battery sample in the j-th second evaluation index, and replace the data with a proportion of 0 with a preset value, which is set to 0.0001. The formula is expressed as: The information entropy of each of the second evaluation indicators is calculated based on the ratio, and the formula is expressed as: The information redundancy value of each of the second evaluation indicators is calculated based on the information entropy, and the formula is expressed as: d j =1-e j The entropy weight of each of the second evaluation indicators is determined based on the information redundancy value, and the formula is expressed as: Among them, i=1,2,…n; j=1,2,…p.

6. The method according to claim 5, characterized in that The step of standardizing the indicator data matrix to obtain a standardized matrix, and constructing a weighted matrix in combination with the entropy weight of each second evaluation indicator includes: The indicator data matrix is ​​standardized to obtain a standardized matrix Z, and each element in the standardized matrix Z is expressed as: Based on the standardized matrix Z and combined with the entropy weight of each of the second evaluation indicators, a weighted matrix Z is constructed * , weight matrix Z * Each element in is represented as: With ij * =from ij ·In j Among them, i=1,2,…n; j=1,2,…p.

7. The method according to claim 6, characterized in that The obtaining of the optimal ideal solution and the worst ideal solution based on the weighted matrix, and obtaining the distances between the target battery to be evaluated and the optimal ideal solution and the worst ideal solution, respectively, to obtain a comprehensive evaluation result of the target battery to be evaluated, includes: Based on the weighted matrix, the optimal ideal solution Z is obtained. *+ : WITH *+ =(z1 *+ ,z2 *+ ,…,With p *+ ) =(max{z 11 * ,With 21 * ,…,With n1 * },max{from 12 * ,With 22 * ,…,With n2 * },…,max{from 1p * ,With 2p * ,…,With np * }) Based on the weighted matrix, the worst ideal solution Z is obtained. *- : WITH *- =(z1 *- ,z2 *- ,…,With p *- ) =(min{z 11 * ,With 21 * ,…,With n1 * },min{z 12 * ,With 22 * ,…,With n2 * },…,min{z 1p * ,With 2p * ,…,With np * }) Get the distance between the i-th battery sample and the optimal ideal solution: Get the distance between the i-th battery sample and the worst ideal solution: Calculate the relative closeness between the i-th battery sample and the optimal ideal solution: Obtaining a sample number of the target battery to be evaluated, and determining a relative closeness between the target battery to be evaluated and the optimal ideal solution according to the sample number; A comprehensive evaluation result of the target battery to be evaluated is generated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution.

8. The method according to claim 7, characterized in that The generating a comprehensive evaluation result of the target battery to be evaluated based on the relative proximity between the target battery to be evaluated and the optimal ideal solution includes: Comparing the relative closeness between the target battery to be evaluated and the optimal ideal solution with a preset relative closeness level interval to obtain a comparison result; The performance level of the target battery to be evaluated is determined based on the comparison result, and the performance level of the target battery to be evaluated is used as a comprehensive evaluation result of the target battery to be evaluated.

9. A device for evaluating the comprehensive performance of a battery, characterized in that: include: An indicator selection module, used to select a plurality of first evaluation indicators and a plurality of second evaluation indicators from the battery performance parameters; A first evaluation module, configured to perform battery performance evaluation on an initial battery to be evaluated based on the plurality of first evaluation indicators, and obtain an initial evaluation result of the initial battery to be evaluated; An acquisition module, configured to acquire, according to the initial evaluation result, an initial battery to be evaluated that passes the initial evaluation as a target battery to be evaluated; A construction module, configured to construct a virtual optimal battery sample and a virtual worst battery sample based on the indicator requirements of the plurality of second evaluation indicators; The second evaluation module is used to establish an entropy-weighted TOPSIS model based on the second evaluation index data corresponding to the target battery to be evaluated, the virtual optimal battery sample and the virtual worst battery sample, so as to obtain a comprehensive evaluation result of the target battery to be evaluated.

10. An electronic device, comprising: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 8.

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